Sunday, 29 January 2017

How to Implement Big Data for Pharmaceutical Supply Chain Management

Big data and predictive analytics are
quickly changing the way most industries do business. By collecting all
industry-related data, even the little pieces of data that might seem insignificant,
companies can predict trends, find patterns and gain an edge in what might be the
most competitive field we’ve ever seen. Pharmaceutical supply chains process
massive amounts of data every day and can benefit from tools that big data
provides. If you’re managing a supply chain, how can you implement big data to
assist in your daily tasks?

Big data provides predictive analytics. It
utilizes algorithms to make sense of the enormous amount of collected data and use
it to make predictions about future events. Individuals and businesses can
make informed decisions about supply chains, purchases, sales and other related
factors using the predictions.

Supply chains have almost always been driven
by a form of analytics, based on past experience and a variety of performance
indicators. And with the implementation of big data, supply
chain managers can make quantifiable predictions which they can later act
upon rather than making changes to the chain after the fact.

First
Steps

Most pharmaceutical supply chains aren’t set
up to jump into a big data implementation plan for one primary reason: the
first step toward a functional big data system is data collection. Tagging the
drugs with barcodes or RFID-enabled tags and tracking them from production to
prescription allows pharmaceutical companies to curb product loss and reduce
the number of fake or unsafe drugs on the market. They
can collect all the data necessary to build the foundations for a big data
and analytics network.

Pharmaceutical
supply chains are complicated, as they’re rife with multiple distribution
levels and regulations. In many ways, it’s like a Jenga tower. If one piece is
moved wrong, the whole thing could topple over. This is why big data will be
one of the most vital tools in the industry.

Predict
Not React

Even when you consider all of the
information that can be collected after-the-fact in regards to pharmaceutical
supply chains, managers are left reacting to situations that arise and often
scrambling to fix problems before they interrupt the supply chain.

With supply chain analytics and a stable big
data system at their command, supply chain
managers can predict with relative accuracy where problems will occur. This
enables them to move from a reactive model to a predictive one, preventing
supply chain interruptions. Predictions aren’t perfect, but they become more
accurate as the system collects more data. Then, the predictions become more
accurate and useful to supply managers.

Implementation

Besides collecting as much information as
possible, you should remember to:

·Involve
everyone. Big data is not something that you can
simply drop on your crew and expect it to work.
It will require a concerted effort from everyone — the workers up to the
CEO.

·Invest
in quality hardware. Don’t skimp on the
hardware that will handle your big data system. DIY big data systems can be built using open-sourced software like
Hadoop, but if the device can’t stand up to the load, your hard work goes
down the virtual tubes.

·Ask
for help. If you’re not technologically inclined,
you will need more than a YouTube tutorial. Look into big data consulting firms
to help you get set up. Consider hiring someone specifically to maintain your
big data system.

Big data is changing the way we look at
information. For pharmaceutical supply chains, in particular, it may change the
way we keep track of our products. Supply chain analytics for pharmaceuticals
is a relatively new field, but it may not be optional for long.